gradio_llm_example.py

import gradio as gr
import time
import random
import os
import shutil
# How to RUN code ==> gradio gradio_llm_example.py 




# Define text and title information 
title1 = "##  </br> </br> </br> 🤗💬 QA App"

title2 = " ##  </br> </br> </br> Gradio QA Bot"

intro = """ Welcome! This is not just any bot, it's a special one equipped with state-of-the-art natural language processing capabilities, and ready to answer your queries.


    Ready to explore? Let's get started!
  
    
    * Step 1: Upload a PDF document.
    * Step 2: Type in a question related to your document's content.
    * Step 3: Get your answer!
 
    
    Push clear cache before uploading a new doc!

    
    """

about = """ 
    ## </br> About
    This app is an LLM-powered chatbot built using:
    - [Streamlit](<https://streamlit.io/>)
    - [HugChat](<https://github.com/Soulter/hugging-chat-api>)
    - Chat Model = llama2-chat-hf 7B 
    - Retreiver model = all-MiniLM-L6-v2

    </br> 
    💡 Note: No API key required!
    
     </br> 
    Made with ❤️ by us
     """


# Define theme ==> see gr.themes.builder()
theme = gr.themes.Soft(
    primary_hue="emerald",
    secondary_hue="emerald",
    neutral_hue="slate",
).set(
    body_background_fill_dark='*primary_50',
    shadow_drop='*shadow_spread',
    button_border_width='*block_border_width',
    button_border_width_dark='*block_label_border_width'
)


def upload_file(files_obj):
    """ Upload several files from drag and drop, and save them in local temp folder
    files_obj (type:list) : list of tempfile._TemporaryFileWrapper 
    return checkbox to display uploaded documents """
    # Create local copy
    temp_file_path = "./temp"
    if not os.path.exists(temp_file_path):
        os.makedirs(temp_file_path)
    # Save each file among list of given files
    file_name_list = list()
    for file_obj in files_obj :
        file_name = os.path.basename(file_obj.name)
        file_name_list.append(file_name)
        shutil.copyfile(file_obj.name, os.path.join(temp_file_path, file_name))
    # return visible button for next selection 
    return {uploaded_check : gr.CheckboxGroup(choices=file_name_list, visible=True),
            choose_btn : gr.Button(value="Choose", visible=True)}


def read_content(content, files_name):
    """ Read and update the content variable (state) according to the several files_names to read from temp folder
    return updated content_var (type : list of str)
    return visible error_box to display logs error """
    content_list = list()
    text_list = list()
    # Parse one or several docs among the selected ones
    for file_name in files_name :
        print(file_name, type(file_name))
        temp_file_path = "./temp"
        file_path = os.path.join(temp_file_path, file_name)
        # Read doc
        with open(file_path, "rb") as file:
            try:
                content = file.read()
                #### YOUR FONCTION FOR CONTENT ==> must be str
                my_content = str(content[:10])
                content_list.append(my_content) 
                text_list.append(f"File {file_name} ready to be used. \n")
                print(content)
            except Exception as e:
                print(f"Error occurred while writing the file: {e}")
                text_list.append(f"Error occurred while writing the file {file_name}: {e}")
    return {content_var : content_list,
            error_box : gr.Textbox(value=f"""{" and ".join(text_list)} \n You can ask a question about the uploaded PDF document.""", visible=True)}


### YOUR model using the same inputand returning output 
def my_model(message, chat_history, content_var,
            language_choice, model_choice, max_length, temperature,
            num_return_sequences, top_p, no_repeat_ngram_size):
        #No LLM here, just respond with a random pre-made message
        if content_var == []:
            bot_message = f"Pas de context : {content_var}" + random.choice(["Tell me more about it", 
                                            "Cool, but I'm not interested", 
                                            "Hmmmm, ok then"]) 
        else:
            bot_message = f" Voici le context {content_var}"
        chat_history.append((message, bot_message))

        return "", chat_history


def queue_bot(history):
    """ For in progress display during chat """
    bot_message = history[-1][1]
    history[-1][1] = ""
    for character in bot_message:
        history[-1][1] += character
        time.sleep(0.05)
        yield history


# App
with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:

    # Initialize the document context variable as empty without any drag and drop
    content_var = gr.State([])

    # Layout
    with gr.Row():
        # Row 1 : About
        with gr.Column(scale=1, min_width=100):
            # gr.Image("./logo_neovision.png")
            logo_gr = gr.Markdown(""" <img src="file/logo_neovision.png" alt="logo" style="width:400px;"/>""") 
            about_gr = gr.Markdown(about)   
        
        # Row 2 : Param
        with gr.Column(scale=2, min_width=500):
            title1_gr= gr.Markdown(title1)
            intro_gr = gr.Markdown(intro)

            #  Upload several documents
            upload_button = gr.UploadButton("Browse files", label="Drag and drop your documents here", 
                                            size="lg", scale=0, min_width=100,
                                            file_types=["pdf"], file_count="multiple")
            # invisible button while no documents uploaded
            uploaded_check = gr.CheckboxGroup(label="Uploaded documents", visible=False, 
                                      info="Do you want to use a supporting document?")
            choose_btn = gr.Button(value="Choose", visible=False)
            # uploading one or several docs and display other buttons
            upload_button.upload(upload_file, upload_button, [uploaded_check, choose_btn])

            # Read only one document
            error_box = gr.Textbox(label="Reading files... ", visible=False) # display only when ready or error
            choose_btn.click(read_content, inputs=[content_var, uploaded_check], outputs=[content_var, error_box])

            # Select advanced options, to be given as input for your model
            gr.Markdown(""" ## Toolbox """)
            with gr.Accordion(label="Select advanced options",open=False):
                model_choice = gr.Dropdown(["LLM", "Other"], label="Model", info="Choose your AI model")
                language_choice = gr.Dropdown(["English", "French"], label="Language", info="Choose your language")
                max_length = gr.Slider(label="Token length", minimum=1, maximum=100, value=50, step=1)
                temperature= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1)
                num_return_sequences= gr.Slider(label="Return Sequence", minimum=0.1, maximum=50, value=1, step=0.1)
                top_p= gr.Slider(label="top p", minimum=0.1, maximum=1, value=0.8, step=0.1)
                no_repeat_ngram_size= gr.Slider(label="repeat", minimum=0.1, maximum=1, value=3, step=0.1)


        # Row 3 : Chat
        with gr.Column(scale=2, min_width=600):
            title2_gr = gr.Markdown(title2)
            chatbot = gr.Chatbot(label="Bot", height=500)
            msg = gr.Textbox(label="User", placeholder="Ask any question.")
            ### YOUR MODEL TO ADAPT
            msg.submit(my_model,
                       inputs=[msg, chatbot, content_var,
                                language_choice, model_choice, max_length, temperature,
                                num_return_sequences, top_p, no_repeat_ngram_size],
                                outputs=[msg, chatbot]).then(queue_bot, chatbot, chatbot)
            clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")


gr.close_all()
gradioApp.queue()
gradioApp.launch(share=True, auth=("neovision", "gradio2023"))
#auth=("neovision", "gradio2023") to be placed inside the launch parameters